This paper analyzes the impact of model complexity on the net present value distribution and the expected default probability of equity investments in project finance. Model complexity is analyzed along two dimensions: simulation complexity and forecast complexity. We aim to identify model elements which are crucial for the valuation of project finance in practice. First, we present a simulatio...

In this research, the DCC model is estimated to calculate dynamic correlation series between crude oil price and growth of Industry and Mine sector during 1367:1-1392:4. Then, Macroeconomic variables which can explain the dynamic correlation are analyzed as variables of contagion. So, the import, as an effective and affected variable from crude oil price, is separated to real import of consumpt...

Recently a new dependence measure, the distance correlation, has been proposed to measure the dependence between continuous random variables. A nice property of this measure is that it can be consistently estimated with the empirical average of the products of certain distances between the sample points. Here we generalize this quantity to measure the conditional dependence between random varia...

The construction of score-driven filters for nonlinear time series models is described, and they are shown to apply over a wide range disciplines. Their theoretical practical advantages other methods highlighted. Topics covered include robust modeling, conditional heteroscedasticity, count data, dynamic correlation association, censoring, circular switching regimes.

This paper analyses the stock market linkages of the selected Central and Eastern European (CEE) markets (Czech Republic – PX, Hungary – BUX and Poland – WIG20) with the Western European stock market represented by the German DAX and studies also the comovement between the individual CEE countries’ stock markets. The dynamic conditional correlation (DCC) models were used to model the co-movemen...

This paper introduces a new multivariate conditional volatility model for returns that utilizes realized covariance matrices. The model decomposes the conditional and realized covariance matrices into standard deviations and correlations matrices. On a first level, the univariate variances are estimated by a modified Generalized Autoregressive Conditional Heteroskedasticity (GARCH) that exploit...

In this paper, we describe the Beatsens Team solution of Emotion in Music task in MediaEval benchmarking campaign 2014. We extracted and designed several sets of features and used continuous conditional random field(CCRF) for dynamic emotion characterization task. The best runs for Pearson correlation are 0.23± 0.56 and 0.12± 0.55 of valence and arousal respectively, for RMSE are 0.12± 0.06 and...